Statistical detection algorithms in fat-tailed hyperspectral background clutter

Proceedings of SPIE - The International Society for Optical Engineering

Volumn 5573, Issue , 2004, Pages 215-225

  Bernhardt, M ^a   Oxford, W J ^b   Clare, P E ^b   Wilkinson, V A ^b   Clarke, D G ^b  

^a Waterfall Solutions Ltd   (United Kingdom)

^b DEFENCE SCIENCE AND TECHNOLOGY LABORATORY   (United Kingdom)

Author keywords

Clutter model; Fat tails; Hyperspectral

Indexed keywords

ALGORITHMS; CLASSIFICATION (OF INFORMATION); MATHEMATICAL MODELS; PROBABILITY DENSITY FUNCTION; RADAR CLUTTER; STATISTICAL METHODS;

ANOMALY DETECTION; CLUTTER MODEL; FAT TAILS; HYPERSPECTRAL BACKGROUNDS;

SIGNAL DETECTION;

EID: 17644378736     PISSN: 0277786X     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1117/12.565537     Document Type: Conference Paper

References (10)

1. D.G. Manolakis, D. Marden, J.P. Kerekes, G.A. Shaw, "Statistics of hyperspectral imaging data", Algorithms for Multispectral, Hyperspectral and Ultraspectral Imagery VII, S.S. Shen, M.R. Descour, vol. 4381, pp308-316, SPIE, Orlando, 2001.
2. D. Marden, D. Manolakis, "Algorithms for hyperspectral imaging data exploitation using non-Gaussian elliptically contoured distributions", Imaging Spectroscopy VIII, S.S. Shen, vol. 4816, SPIE, Seattle, WA, 2002.
3. D. Manolakis, G. Shaw, "Detection algorithms for hyperspectral imaging applications", IEEE Signal Proc. Magazine, pp29-43, IEEE, 2002
4. J-P. Bouchaud, M. Potters, Theory of financial risks, pp 19-20, Cambridge University Press, 2000.
5. E. Jakeman, "On the statistics of K-distributed noise", J. Phys. A, pp31-48, 1980.
6. E. Jakeman, P.N. Pusey, "Significance of K distributions in scattering experiments", Phy. Rev. Lett., vol. 40 no. 9, pp546-550, 1978.
7. R.J.A. Tough, K.D. Ward, "The correlation properties of gamma and other non-Gaussian processes generated by memoryless nonlinear transformation", J. Phys. D., vol. 32, pp3075-3084, 1999.
8. I.S. Reed, X. Yu, "Adaptive multiple-band CFAR detection of an optical pattern with unknown spectral distribution", IEEE Trans, Acoustics, Speech and Signal Processing, vol. 38 no. 10, pp 1760-1770, IEEE, 1990.
9. P. Clare, M. Bernhardt, W. Oxford, S. Murphy, P. Godfree, V. Wilkinson, "A new approach to anomaly detection in hyperspectral images", Algorithms and Technologies for Multispectral, Hyperspectral and Ultraspectral Imagery IX, S.S. Shen, P.E. Lewis, vol. 5093, pp17-27, SPIE, Orlando, 2003.
10. S.G. Beavan, G. Hazel, A.D. Stoker, "Automated Gaussian spectral clustering of hyperspectral data", Algorithms and Technologies for Multispectral, Hyperspectral and Ultraspectral Imagery VIII, S.S. Shen, P.E. Lewis, vol. 4725, pp254-266, SPIE, Orlando, 2002.